Abstract
One of the challenging topics in the field of computer vision is the detection of the stationary/non-stationary objects from a video sequence. The outcome of detection, tracking, and learning must be free from ambiguity. For effectively detecting the moving object, first the background information from the video should be subtracted. However, in the high-definition video, modeling techniques suffer from high computation and memory cost which may lead to a decrease in performance measure such as accuracy and efficiency in identifying the object accurately. It is important to identify the definite structure from a large amount of unstructured data which is a prerequisite problem to be solved. The task of finding the structure from a large amount of data is achieved using Deep Learning ‘which is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text’. The purpose of the paper is to survey the method with which the objects can be efficiently detected from any given video sequence along with the preferable use of the deep learning library.
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Garg, D., Kotecha, K. (2018). Object Detection from Video Sequences Using Deep Learning: An Overview. In: Choudhary, R., Mandal, J., Bhattacharyya, D. (eds) Advanced Computing and Communication Technologies. Advances in Intelligent Systems and Computing, vol 562. Springer, Singapore. https://doi.org/10.1007/978-981-10-4603-2_14
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DOI: https://doi.org/10.1007/978-981-10-4603-2_14
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